CERES, AIRS, Outgoing Longwave Radiation & El Nino

Measurements of outgoing longwave radiation (OLR) are essential for understanding many aspects of climate. Many people are confused about the factors that affect OLR. And its rich variability is often not appreciated.

There have been a number of satellite projects since the late 1970’s, with the highlight (prior to 2001) being the five year period of ERBE.

AIRS & CERES were launched on the NASA AQUA satellite in May 2002. These provide much better quality data, with much better accuracy and resolution.

CERES has three instruments:

Solar Reflected Radiation (Shortwave): 0.3 – 5.0 μm

Window: 8 – 12 μm

Total: 0.3 to > 100 μm

AIRS is an infrared spectrometer/radiometer that covers the 3.7–15.4 μm spectral range with 2378 spectral channels. It runs alongside two microwave instruments (better viewing through clouds): AMSU is a 15-channel microwave radiometer operating between 23 and 89 GHz; HSB is a four-channel microwave radiometer that makes measurements between 150 and 190 GHz.

From Aumann et al (2003):

The simultaneous use of the data from the three instruments provides both new and improved measurements of cloud properties, atmospheric temperature and humidity, and land and ocean skin temperatures, with the accuracy, resolution, and coverage required by numerical weather prediction and climate models.

Among the important datasets that AIRS will contribute to climate studies are as follows:

atmospheric temperature profiles;

sea-surface temperature;

land-surface temperature and emissivity;

relative humidity profiles and total precipitable water vapor;

fractional cloud cover;

cloud spectral IR emissivity;

cloud-top pressure and temperature;

total ozone burden of the atmosphere;

column abundances of minor atmospheric gases such as CO, CH, CO, and N2O;

Here is a comparison of the two measurement systems from Susskind et al (2012) over almost a decade:

From Susskind et al (2012)

Figure 1

The second thing to observe is that the measurements have a bias between the two datasets. But because we have two high accuracy measurement systems on the same satellite we do have a reasonable opportunity to identify the source of the bias (total OLR as shown in the graph is made of many components). If we only had one satellite, and then a new satellite took over with a small time overlap any biases would be much more difficult to identify. Of course, that doesn’t stop many people from trying but success would be much harder to judge.

In this paper, as we might expect, the error sources between the two datasets get considerable discussion. One important point is that version 6 AIRS data (prototyped at the time the paper was written) is much closer to CERES. The second point, probably more interesting, is that once we look at anomaly data the results are very close. We’ll see a number of comparisons as we review what the paper shows.

The authors comment:

Behavior of OLR over this short time period should not be taken in any way as being indicative of what long-term trends might be. The ability to begin to draw potential conclusions as to whether there are long-term drifts with regard to the Earth’s OLR, beyond the effects of normal interannual variability, would require consistent calibrated global observations for a time period of at least 20 years, if not longer. Nevertheless, a very close agreement of the 8-year, 10-month OLR anomaly time series derived using two different instruments in two very different manners is an encouraging result.

It demonstrates that one can have confidence in the 1° x 1° OLR anomaly time series as observed by each instrument over the same time period. The second objective of the paper is to explain why recent values of global mean, and especially tropical mean, OLR have been strongly correlated with El Niño/La Niña variability and why both have decreased over the time period under study.

Why Has OLR Varied?

The authors define the average rate of change (ARC) of an anomaly time series as “the slope of the linear least squares fit of the anomaly time series”.

We can see excellent correlation between the two datasets and we can see that OLR has, on average, decreased over this time period.

Below is a comparison with the El Nino index.

We define the term El Niño Index as the difference of the NOAA monthly mean oceanic Sea Surface Temperature (SST), averaged over the NOAA Niño-4 spatial area 5°N to 5°S latitude and 150°W westward to 160°E longitude, from an 8-year NOAA Niño-4 SST monthly mean climatology which we generated based on use of the same 8 years that we used in the generation of the OLR climatologies.

From Susskind et al (2012)

Figure 2

It gets interesting when we look at the geographical distribution of the OLR changes over this time period:

From Susskind et al (2012)

Figure 3 – Click to Enlarge

We see that the tropics have the larger changes (also seen clearly in figure 2) but that some regions of the tropics have strong positive values and other regions have strong negative values. The grey square square centered on 180 longitude is the Nino-4 region. Values as large as +4 W/m²/decade are found in this region. And values as large as -3 W/m²/decade are found over Indonesia (WPMC region).

Let’s look at the time series to see how these changes in OLR took place:

Figure 4 – Click to Enlarge

The main parameters which affect changes in OLR month to month and year to year are a) surface temperatures b) humidity c) clouds. As temperature increases, OLR increases. As humidity and clouds increase, OLR decreases.

Here are the changes in surface temperature, specific humidity at 500mbar and cloud fraction:

From Susskind (2012)

Figure 5 – Click to Enlarge

So, focusing again on the Nino-4 region, we might expect to find that OLR has decreased because of the surface temperature decrease (lower emission of surface radiation) – or we might expect to find that the OLR has increased because the specific humidity and cloud fraction have decreased (thus allowing more surface and lower atmosphere radiation to make it through to TOA). These are mechanisms pulling in opposite directions.

In fact we see that the reduced specific humidity and cloud fraction have outweighed the effect of the surface temperature decrease. So the physics should be clear (still considering the Nino-4 region) – if surface temperature has decreased and OLR has increased then the explanation is the reduction in “greenhouse” gases (in this case water vapor) and clouds, which contain water.

Correlations

We can see similar relationships through correlations.

The term ENC in the graphs stands for El Nino Correlation. This is essentially the correlation of the time-series data with time-series temperature change in the Nino-4 region (more specifically the Nino-4 temperature less the global temperature).

As the Nino-4 temperature declined over the period in question, a positive correlation means the value declined, while a negative correlation means the value increased.

The first graph below is the geographical distribution of rate of change of surface temperature. Of course we see that the Nino-4 region has been declining in temperature (as already seen in figure 2). The second graph shows this as well, but also indicates that the regions west and east of the Nino-4 region have a stronger (negative) correlation than other areas of larger temperature change (like the arctic region).

The third graph shows that 500 mb humidity has been decreasing in the Nino-4 region, and increasing to the west and east of this region. Likewise for the cloud fraction. And all of these are strongly correlated to the Nino-4 time-series temperature:

From Susskind et al (2012)

Figure 6 – Click to expand

For OLR correlations with Nino-4 temperature we find a strong negative correlation, meaning the OLR has increased in the Nino-4 region. And the opposite – a strong positive correlation – in the highlighted regions to east and west of Nino-4:

From Susskind (2012)

Figure 7 – Click to expand

Note the two highlighted regions

to the west: WPMC, Warm Pool Maritime Continent;

and to the east: EEMA, Equatorial Eastern Pacific and Atlantic Region

We can see the correlations between the global & tropical OLR and the OLR changes in these regions:

Figure 8 – Click to expand

Both WPMC and EEPA regions together explain the reduction over 10 years in OLR. Without these two regions the change is indistinguishable from zero.

Conclusion

This article is interesting for a number of reasons.

It shows the amazing variability of climate – we can see adjacent regions in the tropics with completely opposite changes over 10 years.

OLR has – over the globe – decreased over 10 years. This is a result of the El-Nino phase – at the start of the measurement period we were coming out of a large El-Nino event, and at the end of the measurement period we were in a La Nina event.

The reduction in OLR is explained by the change in the two regions identified, which are themselves strongly correlated to the Nino-4 region.

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21 Responses

First of all, thanks for the hat tip on the Susskind paper, much of the basis for this whole post.

Second, the ERBE project did not last for 5 years, but 14-15 (1985-99).

Third, you compare CERES and AIRS, noting that the former actually measures OLR and that the latter calculate it based on other ‘weather-based’ measurements, finding assurance in their striking similarity/correlation. But you forget to acknowledge the fact that this same assurance can be found when comparing the ISCCP FD with ERBE/ERBS and HIRS in the years between 1983-85 and 2000-02. (I know the post is not about that period, but just saying …)

Fourth and last, good to see that ENSO is given its rightful place in this whole issue. That process, fed by energy from the Sun, is after all what drives global temperatures. Not CO2.

First – I assume a tongue in cheek thanks. Which I can understand from your point of view. Although from my point of view I had that paper and had started working on a post on that topic. Afterwards I found it was one of your papers.. well, hat tip to you.

Second – ERBE – I’ll come back to this one. As you haven’t done your own research I will provide mine.

Third – there is a difference between a principle and a value. You are comparing principles and implying that this means the values also are equivalent.

CERES and AIRS find similar but not exact OLR.

The reason that datasets like ISCCP FD exist is precisely because the measured datasets from that earlier era are not accurate enough from a number of points of view, just one of which is long term drift.

The same principle governs both parallel activities. My question is, what is the accuracy of the pre-CERES era?

If we look at the CERES era, a big step forward in measurement we find interesting commentary..

..Differences in SW trends between the two records are generally smaller than 0.3 Wm-2 per decade. For global LW, CERES Terra shows a 0.63 Wm-2 per decade steeper increase than CERES Aqua. The cause for the drift between Terra and Aqua LW TOA fluxes is due to an artificial discontinuity in the middle of the Aqua record that will be corrected for in the next release (see Appendix 2 for more details)..

..There are now over a decade of cloud, aerosol, radiation and atmospheric state observations from advanced satellite instruments aboard the Terra and Aqua spacecrafts, and close to 5 years of vertical cloud and aerosol profile data from CloudSat and CALIPSO. These data provide a rich resource for studying ERB variations and the underlying processes. With the latest CERES calibration improvements, large-scale top-of-atmosphere (TOA) radiation changes during the past decade are observed to within 0.5 Wm-2 per decade based upon comparisons between CERES Terra and Aqua, and between CERES and SeaWIFS, MODIS and AIRS..

In the (much-improved) CERES era, with 2 CERES instruments on 2 satellites and an AIRS instrument all in direct comparison we can identify 0.5 Wm-2 per decade.

Which is precisely the reason for my asking the questions I did ask and you didn’t answer. What do you believe is the accuracy (absolute and wrt drift) of the pre-CERES satellite observations?

The scanner data has been the most useful for understanding climate because it provides OLR and Reflected SW as a function of location. The non-scanner data, by comparison, provides the total OLR via the “WFOV” = wide field of view, with no spatial breakdown.

The stated long-term drift accuracy for ERBE non-scanner OLR:

The ERBS Nonscanner WFOV calibration stability uncertainty is an order magnitude better than its total uncertainty and is estimated from observations to be on the order of 0.35 W/m2 over the 1985 to 1999 time period of the Edition 3 Revision 1 ERBS data.

This comes from a source I can’t verify – ‘Appendix A’ which appears to be by Wong and Wielicki (two well-known people in this field).

ERBE/ERBS Nonscanner WFOV dataset, prompted
by new findings on the possible effect of small but significant ERBS altitude changes over the 15-yr period, has been completed.

This satellite altitude change and its effects on the top of the atmosphere ERB are discussed in section 2 using the new altitude-corrected WFOV Edition3 dataset. Section 3 presents results on a small WFOV shortwave instrument drift and its effects on the ERB record.

This instrument drift was discovered during the validation of the WFOV Edition3 dataset and is not currently included in the archived WFOV Edition3 data, but a simple correction method is available to the data user to remove this instrument artifact..

..Unlike the ERBE scanner instrument, which sees only small portions of the earth’s surface with a 40-km nadir field of view, the Nonscanner WFOV instrument field of view at satellite altitude contains the entire earth disk and a small ring of surrounding deep space. The amount of energy received at the Nonscanner WFOV instrument is therefore inversely proportional to the square of the distance between the instrument and the earth’s center. As the altitude dropped over the 15-yr period, the Nonscanner WFOV instrument recorded a small steady increase in satellite altitude fluxes

The point of referencing this paper and the quote is that stated accuracy is usually a best-case result. Then reality causes either a revised dataset, or a widened accuracy. It’s rare to find it the other way round, although I believe CERES provided better than expected accuracy.

The annual cycle of the global mean TOA net radiation has a reasonably well-determined peak-to-peak amplitude of approximately 15 W/m2, with maximum positive values occurring in late Southern Hemisphere summer. The nonzero values (a few watts per square meter) of the annual average global mean TOA net radiation found from ERBE and ScaRaB data are an indication of observational uncertainty rather than of global radiative imbalance. The geographic distribution of the LW flux emitted to space (also called outgoing longwave radiation) provides useful information on the overall state of the surface–atmosphere column.

Important progress has been made in establishing the climatology of these quantities, thanks in particular to the Nimbus/ERB (Earth Radiation Budget) and the ERBE missions of NASA, but sampling problems continue to limit accuracy. Strong biases remain likely, especially, but not only, in the shortwave fluxes. Because these biases depend on the particular orbit–instrument combination and SW absolute calibration, and because TOA ERB components are vertically integrated quantities, detection of global climate trends in the ERB is probably illusory.

One can obviously bias trends by picking the difference point in the ENSO cycle as starting and ending dates. Why isn’t it possible to pick starting and ending dates which represent similar states in the ENSO cycle?

That’s a good question. In the case of this paper it is just using the longest time-series available.

The correlation of OLR with El-Niño seems pretty clear – or do you disagree?

For matching up the same states in the El Niño cycle I wonder whether this is possible. After all, it is a chaotic system. How do you define similar states in a chaotic system? We have El Ninos and La Ninas, but they don’t follow a rigid alternative pattern, they have very uneven strengths and durations..

So, the broadband measurements ( back to 1974 ) are probably not as accurate as subsequent systems. Still, it’s interesting to look at the series.
In that series, the two most notable El Nino years (’82-’83 and ’97-’98 ) don’t stand out at all in OLR:

SOD: The correlation with ENSO seems clear, but not how to address it. By choosing an appropriate time in the ENSO cycle, one can get all different kinds of answers about whether surface temperature has “significantly increased” over roughly the last decade, so I would hope they would have avoided that problem here.

To try to match states, one could try one of the various indexes of ENSO. Look at the value of the index for the last month of data and find the earliest month with a similar value. Then look at the value of the index for the first month of data and find the latest month with a similar value. Calculate the trend over both periods and report the range.

Off the top of my head, an ARMA process with drift. That is an ARIMA (p,d,q) process with d constrained to be zero. and all of the AR coefficients must be less than one with a possible linear trend. I think you can make a very strong argument that no weather related process is a random walk. It could be fractionally integrated, but you don’t have a long enough time series to tell.

I’m building an interesting model. I’m not trying to answer any question about El Nino.

I’m adding OLR noise “in the style of” El Nino.

Right now I’ve got a couple of sine waves of different frequencies (8 yrs and 2.5 years) added plus some monthly noise and that’s actually not too bad.

But it’s too regular.

I think I need some frequency variation as well – but if you go from sin(2πft) to sin(2πt (f . random_f_factor)) you get a feature that changes as time increases – higher frequency components appearing later in the time series. There’s an obvious answer but I’ve only just arrived at this point.